Mathilde Paul

Anthropogenic factors and the risk of highly pathogenic avian influenza H5N1: prospects from a spatial-based model.

Beginning in 2003, highly pathogenic avian influenza (HPAI) H5N1 virus spread across Southeast Asia, causing unprecedented epidemics. Thailand was massively infected in 2004 and 2005 and continues today to experience sporadic outbreaks. While research findings suggest that the spread of HPAI H5N1 is influenced primarily by trade patterns, identifying the anthropogenic risk factors involved remains a challenge. In this study, we investigated which anthropogenic factors played a role in the risk of HPAI in Thailand using outbreak data from the “second wave” of the epidemic (3 July 2004 to 5 May 2005) in the country. We first performed a spatial analysis of the relative risk of HPAI H5N1 at the subdistrict level based on a hierarchical Bayesian model. We observed a strong spatial heterogeneity of the relative risk. We then tested a set of potential risk factors in a multivariable linear model. The results confirmed the role of free-grazing ducks and rice-cropping intensity but showed a weak association with fighting cock density. The results also revealed a set of anthropogenic factors significantly linked with the risk of HPAI. High risk was associated strongly with densely populated areas, short distances to a highway junction, and short distances to large cities. These findings highlight a new explanatory pattern for the risk of HPAI and indicate that, in addition to agro-environmental factors, anthropogenic factors play an important role in the spread of H5N1. To limit the spread of future outbreaks, efforts to control the movement of poultry products must be sustained.

Risk factors for highly pathogenic avian influenza (HPAI) H5N1 infection in backyard chicken farms, Thailand

To reduce the risk of highly pathogenic avian influenza (HPAI) H5N1 infection in humans, the pathways by which HPAI is spread in poultry must be determined. Backyard poultry farmers are particularly vulnerable to the threat of HPAI, with both their health and livelihoods at risk. Identifying the risk factors for HPAI infection in backyard farms should allow control measures to be better targeted. To study the risk factors of HPAI H5N1 infection, we carried out a case-control study on backyard chicken farms in Thailand, analyzing 104 case farms and 382 control farms. Data on farming practices and environmental characteristics were analyzed using multivariate logistic regression models. We show that farms where owners bought live chickens from another backyard farm had a higher risk of HPAI H5N1 infection (OR 3.34, 95% CI 1.72-6.47), while those where owners used a disinfectant to clean poultry areas were exposed to lower risk (OR 0.48, 95% CI 0.26-0.87). Our results highlight the important role of the trade of poultry between farms in the transmission of HPAI H5N1, in addition to farming practices and environmental characteristics. Findings from this study may help to tailor prevention measures to the local circumstances of backyard farms in different regions of the world.

Practices associated with Highly Pathogenic Avian Influenza spread in traditional poultry marketing chains: Social and economic perspectives.

In developing countries, smallholder poultry production contributes to food security and poverty alleviation in rural areas. However, traditional poultry marketing chains have been threatened by the epidemics caused by the Highly Pathogenic Avian Influenza (H5N1) virus. The article presents a value chain analysis conducted on the traditional poultry marketing chain in the rural province of Phitsanulok, Thailand. The analysis is based on quantitative data collected on 470 backyard chicken farms, and on qualitative data collected on 28 poultry collectors, slaughterhouses and market retailers, using semi-structured interviews. The article examines the organization of poultry marketing chains in time and space, and shows how this may contribute to the spread of Highly Pathogenic Avian Influenza H5N1 in the small-scale poultry sector. The article also discusses the practices and strategies developed by value chain actors facing poultry mortality, with their economic and social determinants. More broadly, this study also illustrates how value chain analysis can contribute to a better understanding of the complex mechanisms associated with the spread of epidemics in rural communities.

Risk Factors and Characteristics of Low Pathogenic Avian Influenza Virus Isolated from Commercial Poultry in Tunisia

Objective Estimate the seroprevalence of influenza A virus in various commercial poultry farms and evaluate specific risk factors as well as analyze their genetic nature using molecular assays. Materials and Methods This report summarizes the findings of a national survey realized from October 2010 to May 2011 on 800 flocks in 20 governorates. Serum samples were screened for the presence of specific influenza virus antibodies using cELISA test. Additionally, swab samples were tested by real time and conventional RT-PCR and compared with results obtained by others assays. Phylogenetic and genetic analyses of the glycoproteins were established for some strains. Results Out of the 800 chicken and turkey flocks tested by cELISA, 223 showed positive anti-NP antibodies (28.7%, 95% CI: 25.6–32.1). Significantly higher seroprevalence was found among the coastal areas compared to inland and during the autumn and winter. Broiler flocks showed significantly lower seroprevalence than layers and broiler breeders. The influenza virus infection prevalence increased after the laying phase among layer flocks. In addition, AIV seropositivity was significantly associated with low biosecurity measures. The Ag EIA and rRT-PCR tests revealed significantly higher numbers of AI positive samples as compared to cell cultures or egg inoculation. All new strains were subtyped as H9N2 by real time and conventional RT-PCR. Drift mutations, addition or deletion of glycosylation sites were likely to have occurred in the HA and NA glycoproteins of Tunisian strains resulting in multiple new amino acid substitutions. This fact may reflect different evolutionary pressures affecting these glycoproteins. The role of these newly detected substitutions should be tested. Conclusion Our findings highlight the potential risk of AIV to avian health. Strict enforcement of biosecurity measures and possible vaccination of all poultry flocks with continuous monitoring of poultry stations may ensure reduction of AIV prevalence and avoid emergence of more pathogenic strains.

Optimizing early detection of avian influenza H5N1 in backyard and free-range poultry production systems in Thailand

For infectious diseases such as highly pathogenic avian influenza caused by the H5N1 virus (A/H5N1 HP), early warning system is essential. Evaluating the sensitivity of surveillance is a necessary step in ensuring an efficient and sustainable system. Stochastic scenario tree modeling was used here to assess the sensitivity of the A/H5N1 HP surveillance system in backyard and free-grazing duck farms in Thailand. The whole surveillance system for disease detection was modeled with all components and the sensitivity of each component and of the overall system was estimated. Scenarios were tested according to selection of high-risk areas, inclusion of components and sampling procedure, were tested. Nationwide passive surveillance (SSC1) and risk-based clinical X-ray (SSC2) showed a similar sensitivity level, with a median sensitivity ratio of 0.96 (95% CI 0.40-15.00). They both provide higher sensitivity than the X-ray laboratory component (SSC3). With the current surveillance design, the sensitivity of detection of the overall surveillance system when the three components are implemented, was equal to 100% for a farm level prevalence of 0.05% and 82% (95% CI 71-89%) for a level of infection of 3 farms. Findings from this study illustrate the usefulness of scenario-tree modeling to document freedom from diseases in developing countries.

High pathogenicity and low genetic evolution of avian paramyxovirus type I (Newcastle disease virus) isolated from live bird markets in Uganda

BackgroundNewcastle disease is still a serious disease of poultry especially in backyard free-range production systems despite the availability of cross protective vaccines. Healthy-looking poultry from live bird markets have been suspected as a major source of disease spread although limited studies have been conducted to ascertain the presence of the virulent strains in the markets and to understand how they are related to outbreak strains.MethodsThis study evaluated the occurrence of Newcastle disease virus in samples collected from poultry in live bird markets across Uganda. The isolates were pathoyped using standard methods (mean death time (MDT), intracelebral pathogenicity index (ICPI), and sequencing of the fusion protein cleavage site motif) and also phylogenetically analysed after sequencing of the full fusion and hemagglutin-neuraminidase genes. The isolates were classified into genotypes and subgenotypes based on the full fusion protein gene classification system and compared with other strains in the region and world-wide.ResultsVirulent avian paramyxovirus type I (APMV-1) (Newcastle disease virus) was isolated in healthy-looking poultry in live bird markets. The viruses belonged to a new subgenotype, Vd, in genotype V, and clustered together with Tanzania and Kenya strains. They harbored low genetic diversity.ConclusionThe occurrence of virulent AMPV-1 strains in live bird markets may serve as sources of Newcastle disease outbreaks in non-commercial farms.

Agro-environmental determinants of avian influenza circulation: a multisite study in Thailand, Vietnam and Madagascar.

Outbreaks of highly pathogenic avian influenza have occurred and have been studied in a variety of ecological systems. However, differences in the spatial resolution, geographical extent, units of analysis and risk factors examined in these studies prevent their quantitative comparison. This study aimed to develop a high-resolution, comparative study of a common set of agro-environmental determinants of avian influenza viruses (AIV) in domestic poultry in four different environments: (1) lower-Northern Thailand, where H5N1 circulated in 2004–2005, (2) the Red River Delta in Vietnam, where H5N1 is circulating widely, (3) the Vietnam highlands, where sporadic H5N1 outbreaks have occurred, and (4) the Lake Alaotra region in Madagascar, which features remarkable similarities with Asian agro-ecosystems and where low pathogenic avian influenza viruses have been found. We analyzed H5N1 outbreak data in Thailand in parallel with serological data collected on the H5 subtype in Vietnam and on low pathogenic AIV in Madagascar. Several agro-environmental covariates were examined: poultry densities, landscape dominated by rice cultivation, proximity to a water body or major road, and human population density. Relationships between covariates and AIV circulation were explored using spatial generalized linear models. We found that AIV prevalence was negatively associated with distance to the closest water body in the Red River Delta, Vietnam highlands and Madagascar. We also found a positive association between AIV and duck density in the Vietnam highlands and Thailand, and with rice landscapes in Thailand and Madagascar. Our findings confirm the important role of wetlands-rice-ducks ecosystems in the epidemiology of AI in diverse settings. Variables influencing circulation of the H5 subtype in Southeast Asia played a similar role for low pathogenic AIV in Madagascar, indicating that this area may be at risk if a highly virulent strain is introduced.

Estimating spatial and temporal variations of the reproduction number for highly pathogenic avian influenza H5N1 epidemic in Thailand.

Since 2003, highly pathogenic avian influenza (HPAI) H5N1 virus has spread, causing a pandemic with serious economic consequences and public health implications. Quantitative estimates of the spread of HPAI H5N1 are needed to adapt control measures. This study aimed to estimate the variations of the reproduction number R in space and time for the HPAI H5N1 epidemic in Thailand. Transmission between sub-districts was analyzed using three different and complementary methods. Transmission of HPAI H5N1 was intense (R(t)>1) before October 2004, at which point the epidemic started to progressively fade out (R(t)<1). The spread was mainly local, with 75% of the putative distances of transmission less than 32km. The map of the mean standardized ratio of transmitting the infection (sr) showed sub-districts with a high risk of transmitting infection. Findings from this study can contribute to discussions regarding the efficacy of control measures and help target surveillance programs.

Zero-inflated models for identifying disease risk factors when case detection is imperfect: application to highly pathogenic avian influenza H5N1 in Thailand.

Logistic regression models integrating disease presence/absence data are widely used to identify risk factors for a given disease. However, when data arise from imperfect surveillance systems, the interpretation of results is confusing since explanatory variables can be related either to the occurrence of the disease or to the efficiency of the surveillance system. As an alternative, we present spatial and non-spatial zero-inflated Poisson (ZIP) regressions for modelling the number of highly pathogenic avian influenza (HPAI) H5N1 outbreaks that were reported at subdistrict level in Thailand during the second epidemic wave (July 3rd 2004 to May 5th 2005). The spatial ZIP model fitted the data more effectively than its non-spatial version. This model clarified the role of the different variables: for example, results suggested that human population density was not associated with the disease occurrence but was rather associated with the number of reported outbreaks given disease occurrence. In addition, these models allowed estimating that 902 (95% CI 881-922) subdistricts suffered at least one HPAI H5N1 outbreak in Thailand although only 779 were reported to veterinary authorities, leading to a general surveillance sensitivity of 86.4% (95% CI 84.5-88.4). Finally, the outputs of the spatial ZIP model revealed the spatial distribution of the probability that a subdistrict could have been a false negative. The methodology presented here can easily be adapted to other animal health contexts.

Application of loop analysis for the qualitative assessment of surveillance and control in veterinary epidemiology

BackgroundSystems for animal disease mitigation involve both surveillance activities and interventions to control the disease. They are complex organizations that are described by partial or imprecise data, making it difficult to evaluate them or make decisions to improve them. A mathematical method, called loop analysis, can be used to model qualitatively the structure and the behavior of dynamic systems; it relies on the study of the sign of the interactions between the components of the system. This method, currently widely used by ecologists, has to our knowledge never been applied in the context of animal disease mitigation systems. The objective of the study was to assess whether loop analysis could be applied to this new context. We first developed a generic model that restricted the applicability of the method to event-based surveillance systems of endemic diseases, excluding the emergence and eradication phases. Then we chose the mitigation system of highly pathogenic avian influenza (HPAI) H5N1 in Cambodia as an example of such system to study the application of loop analysis to a real disease mitigation system.ResultsBreaking down the generic model, we constructed a 6-variables model to represent the HPAI H5N1 mitigation system in Cambodia. This construction work improved our understanding of this system, highlighting the link between surveillance and control which is unclear in traditional representations of this system. Then we analyzed the effect of the perturbations to this HPAI H5N1 mitigation system that we interpreted in terms of investment in a given compartment. This study suggested that increasing intervention at a local level can optimize the system’s efficiency. Indeed, this perturbation both decreases surveillance and intervention costs and reduces the disease’s occurrence.ConclusionLoop analysis can be applied to disease mitigation systems. Its main strength is that it is easy to design, focusing on the signs of the interactions. It is a simple and flexible tool that could be used as a precursor to large-scale quantitative studies, to support reflection about disease mitigation systems structure and functioning.